Log-ratio methods in mixture models for compositional data sets
dc.contributor.author
dc.date.accessioned
2017-03-16T09:51:49Z
dc.date.available
2017-03-16T09:51:49Z
dc.date.issued
2016
dc.identifier.issn
1696-2281
dc.identifier.uri
dc.description.abstract
When traditional methods are applied to compositional data misleading and incoherent results could be obtained. Finite mixtures of multivariate distributions are becoming increasingly important nowadays. In this paper, traditional strategies to fit a mixture model into compositional data sets are revisited and the major difficulties are detailed. A new proposal using a mixture of distributions defined on orthonormal log-ratio coordinates is introduced. A real data set analysis is presented to illustrate and compare the different methodologies
dc.description.sponsorship
This research was supported by the Ministerio de Economía y Competividad through
the projects “METRICS” and “CoDa-RETOS” (MTM2012-33236; MTM2015-65016-
C2-1-R: MINECO/FEDER,UE) and the Agència de Gestió d’Ajuts Universitaris i de
Recerca (AGAUR: 2014SGR551)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Institut d'Estadística de Catalunya (Idescat)
dc.relation
info:eu-repo/grantAgreement/MINECO//MTM2012-33236/ES/METODOS ESTADISTICOS EN ESPACIOS RESTRINGIDOS/
info:eu-repo/grantAgreement/MINECO//MTM2015-65016-C2-1-R/ES/ANALISIS DE DATOS COMPOSICIONALES Y METODOS RELACIONADOS/
dc.relation.isformatof
Reproducció digital del document publicat a: http://www.raco.cat/index.php/SORT/article/view/316149/406263
dc.relation.ispartof
SORT : statistics and operations research transactions, 2016, vol. 40. núm 2, 349-374
dc.relation.ispartofseries
Articles publicats (D-IMA)
dc.rights
Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.uri
dc.subject
dc.title
Log-ratio methods in mixture models for compositional data sets
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.embargo.terms
Cap
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.ProjectAcronym
dc.identifier.eissn
2013-8830